Overview

Dataset statistics

Number of variables27
Number of observations3553
Missing cells5389
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory749.6 KiB
Average record size in memory216.0 B

Variable types

Categorical14
Numeric13

Alerts

property_type has constant value "rumah"Constant
url has a high cardinality: 3552 distinct valuesHigh cardinality
title has a high cardinality: 3342 distinct valuesHigh cardinality
address has a high cardinality: 397 distinct valuesHigh cardinality
district has a high cardinality: 380 distinct valuesHigh cardinality
facilities has a high cardinality: 2024 distinct valuesHigh cardinality
ads_id has a high cardinality: 3457 distinct valuesHigh cardinality
price_in_rp is highly overall correlated with bedrooms and 5 other fieldsHigh correlation
bedrooms is highly overall correlated with price_in_rp and 3 other fieldsHigh correlation
bathrooms is highly overall correlated with price_in_rp and 5 other fieldsHigh correlation
land_size_m2 is highly overall correlated with price_in_rp and 7 other fieldsHigh correlation
building_size_m2 is highly overall correlated with price_in_rp and 7 other fieldsHigh correlation
maid_bedrooms is highly overall correlated with price_in_rp and 4 other fieldsHigh correlation
maid_bathrooms is highly overall correlated with price_in_rp and 4 other fieldsHigh correlation
building_age is highly overall correlated with land_size_m2 and 2 other fieldsHigh correlation
year_built is highly overall correlated with land_size_m2 and 2 other fieldsHigh correlation
property_condition is highly overall correlated with furnishingHigh correlation
furnishing is highly overall correlated with property_conditionHigh correlation
certificate is highly imbalanced (67.3%)Imbalance
certificate has 141 (4.0%) missing valuesMissing
building_age has 1445 (40.7%) missing valuesMissing
year_built has 1445 (40.7%) missing valuesMissing
property_condition has 246 (6.9%) missing valuesMissing
building_orientation has 1647 (46.4%) missing valuesMissing
furnishing has 387 (10.9%) missing valuesMissing
price_in_rp is highly skewed (γ1 = 24.73565608)Skewed
bedrooms is highly skewed (γ1 = 20.33439704)Skewed
url is uniformly distributedUniform
title is uniformly distributedUniform
ads_id is uniformly distributedUniform
carports has 757 (21.3%) zerosZeros
maid_bedrooms has 2078 (58.5%) zerosZeros
maid_bathrooms has 2313 (65.1%) zerosZeros
building_age has 1051 (29.6%) zerosZeros
garages has 1921 (54.1%) zerosZeros

Reproduction

Analysis started2023-07-11 06:45:54.233563
Analysis finished2023-07-11 06:46:41.972674
Duration47.74 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

url
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3552
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
https://www.rumah123.com/properti/tangerang/hos10121234/#qid~ee681e3d-c9e4-4718-ac4c-a651ac12aa71
 
2
https://www.rumah123.com/properti/bekasi/hos11360272/#qid~213b5619-a399-47b3-bfcf-faaef6b542d5
 
1
https://www.rumah123.com/properti/jakarta-barat/hos4450838/#qid~ee7b0714-b8af-457d-8e16-f025e4b2e8e5
 
1
https://www.rumah123.com/properti/jakarta-selatan/hos10897593/#qid~db4a0dd4-d128-4cdc-898f-1d244eba949e
 
1
https://www.rumah123.com/properti/jakarta-barat/hos8640085/#qid~0b155e4a-0b02-4141-8d37-4c7bf7a39207
 
1
Other values (3547)
3547 

Length

Max length103
Median length102
Mean length95.764706
Min length92

Characters and Unicode

Total characters340252
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3551 ?
Unique (%)99.9%

Sample

1st rowhttps://www.rumah123.com/properti/bekasi/hos11360272/#qid~213b5619-a399-47b3-bfcf-faaef6b542d5
2nd rowhttps://www.rumah123.com/properti/bekasi/hos10680347/#qid~748f5d2d-8d3a-4a4e-a1b4-37c7be7ffc25
3rd rowhttps://www.rumah123.com/properti/bekasi/hos10685867/#qid~f6c2cf9d-44fa-4c1a-8749-8ee96b116e93
4th rowhttps://www.rumah123.com/properti/bekasi/hos10927790/#qid~f6c2cf9d-44fa-4c1a-8749-8ee96b116e93
5th rowhttps://www.rumah123.com/properti/bekasi/hos10785530/#qid~1807f915-9393-4e9c-a6d6-eb3d73117a15

Common Values

ValueCountFrequency (%)
https://www.rumah123.com/properti/tangerang/hos10121234/#qid~ee681e3d-c9e4-4718-ac4c-a651ac12aa71 2
 
0.1%
https://www.rumah123.com/properti/bekasi/hos11360272/#qid~213b5619-a399-47b3-bfcf-faaef6b542d5 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-barat/hos4450838/#qid~ee7b0714-b8af-457d-8e16-f025e4b2e8e5 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-selatan/hos10897593/#qid~db4a0dd4-d128-4cdc-898f-1d244eba949e 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-barat/hos8640085/#qid~0b155e4a-0b02-4141-8d37-4c7bf7a39207 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-barat/hos11163222/#qid~0b155e4a-0b02-4141-8d37-4c7bf7a39207 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-barat/hos11172290/#qid~0b155e4a-0b02-4141-8d37-4c7bf7a39207 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-barat/hos10120187/#qid~1f9919b6-2578-4678-aa91-e24953424d5d 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-barat/hos11139210/#qid~1f9919b6-2578-4678-aa91-e24953424d5d 1
 
< 0.1%
https://www.rumah123.com/properti/jakarta-barat/hos11001422/#qid~9c272ab4-9313-4551-bc35-3884bf64db60 1
 
< 0.1%
Other values (3542) 3542
99.7%

Length

2023-07-11T06:46:42.128666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.rumah123.com/properti/tangerang/hos10121234/#qid~ee681e3d-c9e4-4718-ac4c-a651ac12aa71 2
 
0.1%
https://www.rumah123.com/properti/bekasi/hos11062997/#qid~28f37bc1-ecdd-40b0-9d22-cc1f698abc43 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos9769480/#qid~28f37bc1-ecdd-40b0-9d22-cc1f698abc43 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos10685867/#qid~f6c2cf9d-44fa-4c1a-8749-8ee96b116e93 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos10927790/#qid~f6c2cf9d-44fa-4c1a-8749-8ee96b116e93 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos10785530/#qid~1807f915-9393-4e9c-a6d6-eb3d73117a15 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos11177142/#qid~1807f915-9393-4e9c-a6d6-eb3d73117a15 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos10720117/#qid~35c27ac2-b166-4762-83af-d2178576ef1b 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos10753439/#qid~35c27ac2-b166-4762-83af-d2178576ef1b 1
 
< 0.1%
https://www.rumah123.com/properti/bekasi/hos11127849/#qid~35c27ac2-b166-4762-83af-d2178576ef1b 1
 
< 0.1%
Other values (3542) 3542
99.7%

Most occurring characters

ValueCountFrequency (%)
/ 21318
 
6.3%
1 17411
 
5.1%
a 17195
 
5.1%
- 14921
 
4.4%
r 13531
 
4.0%
3 13082
 
3.8%
o 12947
 
3.8%
t 12928
 
3.8%
2 12661
 
3.7%
e 11885
 
3.5%
Other values (28) 192373
56.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175734
51.6%
Decimal Number 110514
32.5%
Other Punctuation 35530
 
10.4%
Dash Punctuation 14921
 
4.4%
Math Symbol 3553
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17195
 
9.8%
r 13531
 
7.7%
o 12947
 
7.4%
t 12928
 
7.4%
e 11885
 
6.8%
p 11223
 
6.4%
d 10919
 
6.2%
h 10659
 
6.1%
w 10659
 
6.1%
c 10453
 
5.9%
Other values (12) 53335
30.3%
Decimal Number
ValueCountFrequency (%)
1 17411
15.8%
3 13082
11.8%
2 12661
11.5%
4 11700
10.6%
8 9814
8.9%
9 9722
8.8%
0 9578
8.7%
6 9351
8.5%
5 8616
7.8%
7 8579
7.8%
Other Punctuation
ValueCountFrequency (%)
/ 21318
60.0%
. 7106
 
20.0%
: 3553
 
10.0%
# 3553
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 14921
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3553
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 175734
51.6%
Common 164518
48.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17195
 
9.8%
r 13531
 
7.7%
o 12947
 
7.4%
t 12928
 
7.4%
e 11885
 
6.8%
p 11223
 
6.4%
d 10919
 
6.2%
h 10659
 
6.1%
w 10659
 
6.1%
c 10453
 
5.9%
Other values (12) 53335
30.3%
Common
ValueCountFrequency (%)
/ 21318
13.0%
1 17411
10.6%
- 14921
9.1%
3 13082
 
8.0%
2 12661
 
7.7%
4 11700
 
7.1%
8 9814
 
6.0%
9 9722
 
5.9%
0 9578
 
5.8%
6 9351
 
5.7%
Other values (6) 34960
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 21318
 
6.3%
1 17411
 
5.1%
a 17195
 
5.1%
- 14921
 
4.4%
r 13531
 
4.0%
3 13082
 
3.8%
o 12947
 
3.8%
t 12928
 
3.8%
2 12661
 
3.7%
e 11885
 
3.5%
Other values (28) 192373
56.5%

price_in_rp
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct660
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1916848 × 109
Minimum42000000
Maximum5.8 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:42.349977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum42000000
5-th percentile4 × 108
Q18 × 108
median1.5 × 109
Q33.59 × 109
95-th percentile1.55 × 1010
Maximum5.8 × 1011
Range5.79958 × 1011
Interquartile range (IQR)2.79 × 109

Descriptive statistics

Standard deviation1.3750674 × 1010
Coefficient of variation (CV)3.2804647
Kurtosis915.55866
Mean4.1916848 × 109
Median Absolute Deviation (MAD)9 × 108
Skewness24.735656
Sum1.4893056 × 1013
Variance1.8908103 × 1020
MonotonicityNot monotonic
2023-07-11T06:46:42.604229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1350000000 60
 
1.7%
1200000000 54
 
1.5%
3500000000 47
 
1.3%
1500000000 46
 
1.3%
900000000 40
 
1.1%
800000000 39
 
1.1%
4500000000 38
 
1.1%
1300000000 37
 
1.0%
1400000000 36
 
1.0%
1800000000 35
 
1.0%
Other values (650) 3121
87.8%
ValueCountFrequency (%)
42000000 2
0.1%
70000000 1
 
< 0.1%
75000000 1
 
< 0.1%
85000000 3
0.1%
100000000 3
0.1%
125000000 3
0.1%
130000000 1
 
< 0.1%
135000000 2
0.1%
145000000 1
 
< 0.1%
150000000 3
0.1%
ValueCountFrequency (%)
5.8 × 10111
< 0.1%
2.5 × 10111
< 0.1%
1.75 × 10112
0.1%
1.5 × 10111
< 0.1%
1.1 × 10111
< 0.1%
1.08 × 10111
< 0.1%
1.06 × 10111
< 0.1%
1 × 10111
< 0.1%
8.5 × 10102
0.1%
8.28 × 10101
< 0.1%

title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3342
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
Sentul City,bogor
 
21
Gracia Ayu Rumah 2 Lantai 3 Kamar Tidur Strategis 2 Menit Stasiun Cibitung
 
10
Rumah 2 Lantai Bagus Semi Furnished SHM di Sentul City , Bogor
 
9
Gracia Ayu Rumah 2 Lantai 3 Kamar Tidur Strategis 8 Menit Tol Cibitung
 
8
Rumah Mewah 2 Lantai Model Scandinavian Modern Cinere Krukut. Lokasi 50 Meter Dari Jalan Raya Krukut
 
5
Other values (3337)
3500 

Length

Max length255
Median length143
Mean length58.270476
Min length9

Characters and Unicode

Total characters207035
Distinct characters86
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3187 ?
Unique (%)89.7%

Sample

1st rowRumah cantik Sumarecon Bekasi Lingkungan asri, tenang & nyaman
2nd rowRumah Kekinian, Magenta Summarecon Bekasi
3rd rowRumah Cantik 2 Lantai Cluster Bluebell Summarecon Bekasi
4th rowRumah Mewah 2Lantai L10x18 C di Cluster VERNONIA Summarecon Bekasi..
5th rowRumah Hoek di Cluster Maple Summarecon Bekasi, Bekasi

Common Values

ValueCountFrequency (%)
Sentul City,bogor 21
 
0.6%
Gracia Ayu Rumah 2 Lantai 3 Kamar Tidur Strategis 2 Menit Stasiun Cibitung 10
 
0.3%
Rumah 2 Lantai Bagus Semi Furnished SHM di Sentul City , Bogor 9
 
0.3%
Gracia Ayu Rumah 2 Lantai 3 Kamar Tidur Strategis 8 Menit Tol Cibitung 8
 
0.2%
Rumah Mewah 2 Lantai Model Scandinavian Modern Cinere Krukut. Lokasi 50 Meter Dari Jalan Raya Krukut 5
 
0.1%
Rumah Bagus Unfurnished SHM di Sentul City , Bogor 5
 
0.1%
Rumah 2 Lantai Baru Unfurnished di Sentul City , Bogor 3
 
0.1%
Andre Tjhia Meruya Luxurious House High Celling Fasad Sangat Istimewa 3
 
0.1%
Rumah Bagus Semi Furnished SHM di Sentul City , Bogor 3
 
0.1%
Rumah Lux 2 Lantai Brand New Dekat ke Stasiun Depok Lama 3
 
0.1%
Other values (3332) 3483
98.0%

Length

2023-07-11T06:46:42.879970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rumah 3235
 
9.2%
di 2039
 
5.8%
siap 876
 
2.5%
lantai 792
 
2.3%
2 780
 
2.2%
harga 743
 
2.1%
dekat 741
 
2.1%
kpr 592
 
1.7%
huni 575
 
1.6%
all 553
 
1.6%
Other values (2636) 24138
68.8%

Most occurring characters

ValueCountFrequency (%)
31653
 
15.3%
a 25265
 
12.2%
i 12680
 
6.1%
n 10273
 
5.0%
e 9461
 
4.6%
u 9436
 
4.6%
r 8974
 
4.3%
t 7159
 
3.5%
m 6115
 
3.0%
l 5831
 
2.8%
Other values (76) 80188
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 132567
64.0%
Uppercase Letter 38084
 
18.4%
Space Separator 31653
 
15.3%
Decimal Number 3008
 
1.5%
Other Punctuation 1527
 
0.7%
Dash Punctuation 75
 
< 0.1%
Math Symbol 55
 
< 0.1%
Control 20
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 15
 
< 0.1%
Other values (2) 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 25265
19.1%
i 12680
9.6%
n 10273
 
7.7%
e 9461
 
7.1%
u 9436
 
7.1%
r 8974
 
6.8%
t 7159
 
5.4%
m 6115
 
4.6%
l 5831
 
4.4%
s 5787
 
4.4%
Other values (16) 31586
23.8%
Uppercase Letter
ValueCountFrequency (%)
R 4413
 
11.6%
S 3421
 
9.0%
A 3215
 
8.4%
B 2921
 
7.7%
D 2501
 
6.6%
M 2377
 
6.2%
H 2311
 
6.1%
L 1915
 
5.0%
C 1906
 
5.0%
T 1896
 
5.0%
Other values (16) 11208
29.4%
Other Punctuation
ValueCountFrequency (%)
, 954
62.5%
. 300
 
19.6%
! 173
 
11.3%
: 20
 
1.3%
& 20
 
1.3%
/ 19
 
1.2%
% 15
 
1.0%
@ 11
 
0.7%
' 7
 
0.5%
* 5
 
0.3%
Other values (2) 3
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 1116
37.1%
1 485
16.1%
0 338
 
11.2%
5 285
 
9.5%
3 258
 
8.6%
8 136
 
4.5%
4 123
 
4.1%
6 99
 
3.3%
7 97
 
3.2%
9 71
 
2.4%
Math Symbol
ValueCountFrequency (%)
+ 20
36.4%
| 19
34.5%
~ 16
29.1%
Other Number
ValueCountFrequency (%)
² 9
90.0%
¼ 1
 
10.0%
Other Symbol
ValueCountFrequency (%)
✅ 3
50.0%
✓ 3
50.0%
Space Separator
ValueCountFrequency (%)
31653
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Control
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 170651
82.4%
Common 36384
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 25265
 
14.8%
i 12680
 
7.4%
n 10273
 
6.0%
e 9461
 
5.5%
u 9436
 
5.5%
r 8974
 
5.3%
t 7159
 
4.2%
m 6115
 
3.6%
l 5831
 
3.4%
s 5787
 
3.4%
Other values (42) 69670
40.8%
Common
ValueCountFrequency (%)
31653
87.0%
2 1116
 
3.1%
, 954
 
2.6%
1 485
 
1.3%
0 338
 
0.9%
. 300
 
0.8%
5 285
 
0.8%
3 258
 
0.7%
! 173
 
0.5%
8 136
 
0.4%
Other values (24) 686
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207019
> 99.9%
None 10
 
< 0.1%
Dingbats 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31653
 
15.3%
a 25265
 
12.2%
i 12680
 
6.1%
n 10273
 
5.0%
e 9461
 
4.6%
u 9436
 
4.6%
r 8974
 
4.3%
t 7159
 
3.5%
m 6115
 
3.0%
l 5831
 
2.8%
Other values (72) 80172
38.7%
None
ValueCountFrequency (%)
² 9
90.0%
¼ 1
 
10.0%
Dingbats
ValueCountFrequency (%)
✅ 3
50.0%
✓ 3
50.0%

address
Categorical

Distinct397
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
Sentul City, Bogor
 
282
Alam Sutera, Tangerang
 
115
Gading Serpong, Tangerang
 
97
Pantai Indah Kapuk, Jakarta Utara
 
94
BSD, Tangerang
 
83
Other values (392)
2882 

Length

Max length43
Median length34
Mean length19.794258
Min length11

Characters and Unicode

Total characters70329
Distinct characters50
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)3.8%

Sample

1st rowSummarecon Bekasi, Bekasi
2nd rowSummarecon Bekasi, Bekasi
3rd rowSummarecon Bekasi, Bekasi
4th rowSummarecon Bekasi, Bekasi
5th rowSummarecon Bekasi, Bekasi

Common Values

ValueCountFrequency (%)
Sentul City, Bogor 282
 
7.9%
Alam Sutera, Tangerang 115
 
3.2%
Gading Serpong, Tangerang 97
 
2.7%
Pantai Indah Kapuk, Jakarta Utara 94
 
2.6%
BSD, Tangerang 83
 
2.3%
BSD City, Tangerang 76
 
2.1%
Sawangan, Depok 75
 
2.1%
Harapan Indah, Bekasi 74
 
2.1%
Cinere, Depok 68
 
1.9%
Cibinong, Bogor 66
 
1.9%
Other values (387) 2523
71.0%

Length

2023-07-11T06:46:43.139498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogor 899
 
9.1%
tangerang 865
 
8.7%
bekasi 712
 
7.2%
jakarta 709
 
7.2%
depok 529
 
5.4%
city 378
 
3.8%
sentul 295
 
3.0%
selatan 258
 
2.6%
bsd 257
 
2.6%
barat 230
 
2.3%
Other values (392) 4755
48.1%

Most occurring characters

ValueCountFrequency (%)
a 11054
15.7%
6334
 
9.0%
n 5184
 
7.4%
r 4517
 
6.4%
e 4141
 
5.9%
g 4082
 
5.8%
, 3553
 
5.1%
o 3377
 
4.8%
i 3158
 
4.5%
t 2962
 
4.2%
Other values (40) 21967
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50034
71.1%
Uppercase Letter 10406
 
14.8%
Space Separator 6334
 
9.0%
Other Punctuation 3553
 
5.1%
Decimal Number 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11054
22.1%
n 5184
10.4%
r 4517
9.0%
e 4141
 
8.3%
g 4082
 
8.2%
o 3377
 
6.7%
i 3158
 
6.3%
t 2962
 
5.9%
k 2578
 
5.2%
u 1870
 
3.7%
Other values (14) 7111
14.2%
Uppercase Letter
ValueCountFrequency (%)
B 2345
22.5%
S 1389
13.3%
T 1165
11.2%
C 1148
11.0%
J 1010
9.7%
D 863
 
8.3%
P 529
 
5.1%
K 404
 
3.9%
G 354
 
3.4%
I 214
 
2.1%
Other values (12) 985
9.5%
Space Separator
ValueCountFrequency (%)
6334
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3553
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60440
85.9%
Common 9889
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11054
18.3%
n 5184
 
8.6%
r 4517
 
7.5%
e 4141
 
6.9%
g 4082
 
6.8%
o 3377
 
5.6%
i 3158
 
5.2%
t 2962
 
4.9%
k 2578
 
4.3%
B 2345
 
3.9%
Other values (36) 17042
28.2%
Common
ValueCountFrequency (%)
6334
64.1%
, 3553
35.9%
3 1
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11054
15.7%
6334
 
9.0%
n 5184
 
7.4%
r 4517
 
6.4%
e 4141
 
5.9%
g 4082
 
5.8%
, 3553
 
5.1%
o 3377
 
4.8%
i 3158
 
4.5%
t 2962
 
4.2%
Other values (40) 21967
31.2%

district
Categorical

Distinct380
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
Sentul City
 
282
Alam Sutera
 
115
Gading Serpong
 
97
Pantai Indah Kapuk
 
94
BSD
 
83
Other values (375)
2882 

Length

Max length32
Median length25
Mean length9.9397692
Min length3

Characters and Unicode

Total characters35316
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)3.6%

Sample

1st rowSummarecon Bekasi
2nd rowSummarecon Bekasi
3rd rowSummarecon Bekasi
4th rowSummarecon Bekasi
5th rowSummarecon Bekasi

Common Values

ValueCountFrequency (%)
Sentul City 282
 
7.9%
Alam Sutera 115
 
3.2%
Gading Serpong 97
 
2.7%
Pantai Indah Kapuk 94
 
2.6%
BSD 83
 
2.3%
BSD City 76
 
2.1%
Sawangan 75
 
2.1%
Harapan Indah 74
 
2.1%
Cinere 70
 
2.0%
Cibinong 66
 
1.9%
Other values (370) 2521
71.0%

Length

2023-07-11T06:46:43.372323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 378
 
6.7%
sentul 295
 
5.2%
bsd 257
 
4.6%
indah 191
 
3.4%
gading 127
 
2.3%
bekasi 126
 
2.2%
sutera 121
 
2.2%
alam 118
 
2.1%
serpong 114
 
2.0%
pantai 101
 
1.8%
Other values (390) 3797
67.5%

Most occurring characters

ValueCountFrequency (%)
a 5449
15.4%
n 3242
 
9.2%
i 2477
 
7.0%
2072
 
5.9%
e 1938
 
5.5%
u 1737
 
4.9%
r 1645
 
4.7%
t 1639
 
4.6%
g 1499
 
4.2%
S 1149
 
3.3%
Other values (39) 12469
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27098
76.7%
Uppercase Letter 6144
 
17.4%
Space Separator 2072
 
5.9%
Decimal Number 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5449
20.1%
n 3242
12.0%
i 2477
9.1%
e 1938
 
7.2%
u 1737
 
6.4%
r 1645
 
6.1%
t 1639
 
6.0%
g 1499
 
5.5%
o 1089
 
4.0%
l 889
 
3.3%
Other values (14) 5494
20.3%
Uppercase Letter
ValueCountFrequency (%)
S 1149
18.7%
C 1148
18.7%
B 672
10.9%
P 491
8.0%
K 404
 
6.6%
G 354
 
5.8%
D 337
 
5.5%
J 301
 
4.9%
T 219
 
3.6%
I 214
 
3.5%
Other values (12) 855
13.9%
Space Separator
ValueCountFrequency (%)
2072
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33242
94.1%
Common 2074
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5449
16.4%
n 3242
 
9.8%
i 2477
 
7.5%
e 1938
 
5.8%
u 1737
 
5.2%
r 1645
 
4.9%
t 1639
 
4.9%
g 1499
 
4.5%
S 1149
 
3.5%
C 1148
 
3.5%
Other values (36) 11319
34.1%
Common
ValueCountFrequency (%)
2072
99.9%
3 1
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5449
15.4%
n 3242
 
9.2%
i 2477
 
7.0%
2072
 
5.9%
e 1938
 
5.5%
u 1737
 
4.9%
r 1645
 
4.7%
t 1639
 
4.6%
g 1499
 
4.2%
S 1149
 
3.3%
Other values (39) 12469
35.3%

city
Categorical

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
Bogor
881 
Tangerang
851 
Bekasi
586 
Depok
526 
Jakarta Selatan
240 
Other values (4)
469 

Length

Max length16
Median length14
Mean length8.8544892
Min length6

Characters and Unicode

Total characters31460
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Bekasi
2nd row Bekasi
3rd row Bekasi
4th row Bekasi
5th row Bekasi

Common Values

ValueCountFrequency (%)
Bogor 881
24.8%
Tangerang 851
24.0%
Bekasi 586
16.5%
Depok 526
14.8%
Jakarta Selatan 240
 
6.8%
Jakarta Barat 206
 
5.8%
Jakarta Utara 130
 
3.7%
Jakarta Timur 95
 
2.7%
Jakarta Pusat 38
 
1.1%

Length

2023-07-11T06:46:43.594939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T06:46:43.852555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bogor 881
20.7%
tangerang 851
20.0%
jakarta 709
16.6%
bekasi 586
13.7%
depok 526
12.3%
selatan 240
 
5.6%
barat 206
 
4.8%
utara 130
 
3.1%
timur 95
 
2.2%
pusat 38
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a 5605
17.8%
4262
13.5%
r 2872
9.1%
g 2583
8.2%
o 2288
7.3%
e 2203
 
7.0%
n 1942
 
6.2%
k 1821
 
5.8%
B 1673
 
5.3%
t 1323
 
4.2%
Other values (12) 4888
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22936
72.9%
Space Separator 4262
 
13.5%
Uppercase Letter 4262
 
13.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5605
24.4%
r 2872
12.5%
g 2583
11.3%
o 2288
10.0%
e 2203
 
9.6%
n 1942
 
8.5%
k 1821
 
7.9%
t 1323
 
5.8%
i 681
 
3.0%
s 624
 
2.7%
Other values (4) 994
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
B 1673
39.3%
T 946
22.2%
J 709
16.6%
D 526
 
12.3%
S 240
 
5.6%
U 130
 
3.1%
P 38
 
0.9%
Space Separator
ValueCountFrequency (%)
4262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27198
86.5%
Common 4262
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5605
20.6%
r 2872
10.6%
g 2583
9.5%
o 2288
8.4%
e 2203
 
8.1%
n 1942
 
7.1%
k 1821
 
6.7%
B 1673
 
6.2%
t 1323
 
4.9%
T 946
 
3.5%
Other values (11) 3942
14.5%
Common
ValueCountFrequency (%)
4262
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5605
17.8%
4262
13.5%
r 2872
9.1%
g 2583
8.2%
o 2288
7.3%
e 2203
 
7.0%
n 1942
 
6.2%
k 1821
 
5.8%
B 1673
 
5.3%
t 1323
 
4.2%
Other values (12) 4888
15.5%

lat
Real number (ℝ)

Distinct389
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.3247212
Minimum-6.8948278
Maximum-6.1024783
Zeros0
Zeros (%)0.0%
Negative3553
Negative (%)100.0%
Memory size27.9 KiB
2023-07-11T06:46:44.108222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.8948278
5-th percentile-6.544858
Q1-6.397933
median-6.3007333
Q3-6.231754
95-th percentile-6.1461656
Maximum-6.1024783
Range0.7923495
Interquartile range (IQR)0.166179

Descriptive statistics

Standard deviation0.12924525
Coefficient of variation (CV)-0.020434932
Kurtosis-0.058340186
Mean-6.3247212
Median Absolute Deviation (MAD)0.0875163
Skewness-0.5762973
Sum-22471.734
Variance0.016704334
MonotonicityNot monotonic
2023-07-11T06:46:44.356887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.5183495 293
 
8.2%
-6.3007333 159
 
4.5%
-6.2623791 115
 
3.2%
-6.2433511 97
 
2.7%
-6.1024783 94
 
2.6%
-6.397933 75
 
2.1%
-6.1817523 74
 
2.1%
-6.332401 70
 
2.0%
-6.4824535 66
 
1.9%
-6.387334 52
 
1.5%
Other values (379) 2458
69.2%
ValueCountFrequency (%)
-6.8948278 3
 
0.1%
-6.7648805 2
 
0.1%
-6.7213695 8
0.2%
-6.7028187 6
0.2%
-6.6854146 1
 
< 0.1%
-6.681057 1
 
< 0.1%
-6.67582 5
0.1%
-6.6717395 6
0.2%
-6.6655435 3
 
0.1%
-6.6622971 1
 
< 0.1%
ValueCountFrequency (%)
-6.1024783 94
2.6%
-6.1096059 5
 
0.1%
-6.10988 7
 
0.2%
-6.115513 1
 
< 0.1%
-6.116632 3
 
0.1%
-6.1204935 1
 
< 0.1%
-6.12097 19
 
0.5%
-6.1230849 2
 
0.1%
-6.1259024 1
 
< 0.1%
-6.1263046 22
 
0.6%

long
Real number (ℝ)

Distinct390
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.79288
Minimum106.40231
Maximum109.77169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:44.623188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum106.40231
5-th percentile106.52202
Q1106.68729
median106.79995
Q3106.87477
95-th percentile107.00644
Maximum109.77169
Range3.3693769
Interquartile range (IQR)0.1874711

Descriptive statistics

Standard deviation0.17215909
Coefficient of variation (CV)0.0016120839
Kurtosis79.662912
Mean106.79288
Median Absolute Deviation (MAD)0.091245
Skewness4.7662812
Sum379435.11
Variance0.029638752
MonotonicityNot monotonic
2023-07-11T06:46:44.853673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.8512789 293
 
8.2%
106.586126 159
 
4.5%
106.5220235 115
 
3.2%
106.6174456 97
 
2.7%
106.7411903 94
 
2.6%
106.7679611 75
 
2.1%
106.9736839 74
 
2.1%
106.7907829 70
 
2.0%
106.8367054 66
 
1.9%
106.6872949 52
 
1.5%
Other values (380) 2458
69.2%
ValueCountFrequency (%)
106.4023145 2
 
0.1%
106.41202 3
 
0.1%
106.4368779 3
 
0.1%
106.469015 3
 
0.1%
106.4868765 1
 
< 0.1%
106.4879949 1
 
< 0.1%
106.49494 3
 
0.1%
106.4977165 15
0.4%
106.5037491 32
0.9%
106.5131048 25
0.7%
ValueCountFrequency (%)
109.7716914 3
 
0.1%
108.5160663 2
 
0.1%
107.1542629 7
 
0.2%
107.1349794 1
 
< 0.1%
107.1247334 7
 
0.2%
107.1176339 3
 
0.1%
107.1036725 4
 
0.1%
107.0622475 6
 
0.2%
107.055555 21
0.6%
107.0535555 10
0.3%

facilities
Categorical

Distinct2024
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
Keamanan, Taman
400 
Taman, Keamanan
 
173
Keamanan 24 jam
 
57
Taman
 
46
Jalur Telepon
 
37
Other values (2019)
2840 

Length

Max length394
Median length272
Mean length69.616099
Min length2

Characters and Unicode

Total characters247346
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1653 ?
Unique (%)46.5%

Sample

1st rowTempat Jemuran, Jalur Telepon, Taman, Taman
2nd rowTaman
3rd row Jogging Track, Kolam Renang, Masjid, Taman, Jalur Telepon, Keamanan
4th row Jalur Telepon, Jogging Track, Track Lari, Kolam Renang, Kolam Renang, Taman, Taman, CCTV, Jalur Telepon, Keamanan
5th row Jogging Track, Kolam Renang, Taman, Jalur Telepon, Keamanan

Common Values

ValueCountFrequency (%)
Keamanan, Taman 400
 
11.3%
Taman, Keamanan 173
 
4.9%
Keamanan 24 jam 57
 
1.6%
Taman 46
 
1.3%
Jalur Telepon 37
 
1.0%
Keamanan 34
 
1.0%
Tempat Jemuran 22
 
0.6%
Ac 17
 
0.5%
Taman, Tempat Jemuran, Keamanan 24 jam 15
 
0.4%
AC 15
 
0.4%
Other values (2014) 2737
77.0%

Length

2023-07-11T06:46:45.133560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
keamanan 3719
 
10.6%
taman 3718
 
10.6%
track 2099
 
6.0%
ac 1962
 
5.6%
jalur 1601
 
4.6%
telepon 1601
 
4.6%
jam 1365
 
3.9%
24 1364
 
3.9%
tempat 1342
 
3.8%
cctv 1283
 
3.7%
Other values (98) 14908
42.6%

Most occurring characters

ValueCountFrequency (%)
40529
16.4%
a 37439
15.1%
n 21250
 
8.6%
, 18699
 
7.6%
e 15448
 
6.2%
m 13395
 
5.4%
T 10390
 
4.2%
r 7469
 
3.0%
l 6164
 
2.5%
g 5880
 
2.4%
Other values (45) 70683
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 148347
60.0%
Space Separator 40529
 
16.4%
Uppercase Letter 37012
 
15.0%
Other Punctuation 18699
 
7.6%
Decimal Number 2757
 
1.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37439
25.2%
n 21250
14.3%
e 15448
10.4%
m 13395
 
9.0%
r 7469
 
5.0%
l 6164
 
4.2%
g 5880
 
4.0%
i 5088
 
3.4%
t 4861
 
3.3%
o 4659
 
3.1%
Other values (14) 26694
18.0%
Uppercase Letter
ValueCountFrequency (%)
T 10390
28.1%
K 5443
14.7%
C 3711
 
10.0%
J 3689
 
10.0%
A 2569
 
6.9%
L 2033
 
5.5%
V 1421
 
3.8%
M 1294
 
3.5%
S 1235
 
3.3%
P 1071
 
2.9%
Other values (10) 4156
 
11.2%
Decimal Number
ValueCountFrequency (%)
2 1376
49.9%
4 1367
49.6%
1 7
 
0.3%
0 3
 
0.1%
5 2
 
0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
40529
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18699
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185359
74.9%
Common 61987
 
25.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37439
20.2%
n 21250
 
11.5%
e 15448
 
8.3%
m 13395
 
7.2%
T 10390
 
5.6%
r 7469
 
4.0%
l 6164
 
3.3%
g 5880
 
3.2%
K 5443
 
2.9%
i 5088
 
2.7%
Other values (34) 57393
31.0%
Common
ValueCountFrequency (%)
40529
65.4%
, 18699
30.2%
2 1376
 
2.2%
4 1367
 
2.2%
1 7
 
< 0.1%
0 3
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
( 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40529
16.4%
a 37439
15.1%
n 21250
 
8.6%
, 18699
 
7.6%
e 15448
 
6.2%
m 13395
 
5.4%
T 10390
 
4.2%
r 7469
 
3.0%
l 6164
 
2.5%
g 5880
 
2.4%
Other values (45) 70683
28.6%

property_type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size27.9 KiB
rumah
3552 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters17760
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrumah
2nd rowrumah
3rd rowrumah
4th rowrumah
5th rowrumah

Common Values

ValueCountFrequency (%)
rumah 3552
> 99.9%
(Missing) 1
 
< 0.1%

Length

2023-07-11T06:46:45.368695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T06:46:45.559607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rumah 3552
100.0%

Most occurring characters

ValueCountFrequency (%)
r 3552
20.0%
u 3552
20.0%
m 3552
20.0%
a 3552
20.0%
h 3552
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17760
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3552
20.0%
u 3552
20.0%
m 3552
20.0%
a 3552
20.0%
h 3552
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 3552
20.0%
u 3552
20.0%
m 3552
20.0%
a 3552
20.0%
h 3552
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 3552
20.0%
u 3552
20.0%
m 3552
20.0%
a 3552
20.0%
h 3552
20.0%

ads_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3457
Distinct (%)97.4%
Missing4
Missing (%)0.1%
Memory size27.9 KiB
hos11004265
 
3
hos11368613
 
3
hos11368227
 
3
hos11074240
 
2
hos11279294
 
2
Other values (3452)
3536 

Length

Max length11
Median length11
Mean length10.910116
Min length10

Characters and Unicode

Total characters38720
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3368 ?
Unique (%)94.9%

Sample

1st rowhos11360272
2nd rowhos10680347
3rd rowhos10685867
4th rowhos10927790
5th rowhos10785530

Common Values

ValueCountFrequency (%)
hos11004265 3
 
0.1%
hos11368613 3
 
0.1%
hos11368227 3
 
0.1%
hos11074240 2
 
0.1%
hos11279294 2
 
0.1%
hos10990873 2
 
0.1%
hos11362404 2
 
0.1%
hos11366720 2
 
0.1%
hos10120187 2
 
0.1%
hos10966632 2
 
0.1%
Other values (3447) 3526
99.2%
(Missing) 4
 
0.1%

Length

2023-07-11T06:46:45.743271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hos11004265 3
 
0.1%
hos11368227 3
 
0.1%
hos11368613 3
 
0.1%
hos11356303 2
 
0.1%
hos11030932 2
 
0.1%
hos11334044 2
 
0.1%
hos11335357 2
 
0.1%
hos8169216 2
 
0.1%
hos11246635 2
 
0.1%
hos11201897 2
 
0.1%
Other values (3447) 3526
99.4%

Most occurring characters

ValueCountFrequency (%)
1 7681
19.8%
h 3549
9.2%
o 3549
9.2%
s 3549
9.2%
0 3120
8.1%
3 2572
 
6.6%
6 2465
 
6.4%
2 2268
 
5.9%
9 2198
 
5.7%
8 2129
 
5.5%
Other values (3) 5640
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28073
72.5%
Lowercase Letter 10647
 
27.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7681
27.4%
0 3120
11.1%
3 2572
 
9.2%
6 2465
 
8.8%
2 2268
 
8.1%
9 2198
 
7.8%
8 2129
 
7.6%
4 1909
 
6.8%
5 1903
 
6.8%
7 1828
 
6.5%
Lowercase Letter
ValueCountFrequency (%)
h 3549
33.3%
o 3549
33.3%
s 3549
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 28073
72.5%
Latin 10647
 
27.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7681
27.4%
0 3120
11.1%
3 2572
 
9.2%
6 2465
 
8.8%
2 2268
 
8.1%
9 2198
 
7.8%
8 2129
 
7.6%
4 1909
 
6.8%
5 1903
 
6.8%
7 1828
 
6.5%
Latin
ValueCountFrequency (%)
h 3549
33.3%
o 3549
33.3%
s 3549
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7681
19.8%
h 3549
9.2%
o 3549
9.2%
s 3549
9.2%
0 3120
8.1%
3 2572
 
6.6%
6 2465
 
6.4%
2 2268
 
5.9%
9 2198
 
5.7%
8 2129
 
5.5%
Other values (3) 5640
14.6%

bedrooms
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct22
Distinct (%)0.6%
Missing34
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean3.3265132
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:45.961036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6721479
Coefficient of variation (CV)0.80328791
Kurtosis590.29081
Mean3.3265132
Median Absolute Deviation (MAD)1
Skewness20.334397
Sum11706
Variance7.1403742
MonotonicityNot monotonic
2023-07-11T06:46:46.170896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 1379
38.8%
2 957
26.9%
4 802
22.6%
5 218
 
6.1%
6 93
 
2.6%
1 19
 
0.5%
7 14
 
0.4%
8 10
 
0.3%
10 5
 
0.1%
12 5
 
0.1%
Other values (12) 17
 
0.5%
(Missing) 34
 
1.0%
ValueCountFrequency (%)
1 19
 
0.5%
2 957
26.9%
3 1379
38.8%
4 802
22.6%
5 218
 
6.1%
6 93
 
2.6%
7 14
 
0.4%
8 10
 
0.3%
9 2
 
0.1%
10 5
 
0.1%
ValueCountFrequency (%)
99 1
< 0.1%
57 1
< 0.1%
54 1
< 0.1%
50 1
< 0.1%
37 1
< 0.1%
36 1
< 0.1%
21 1
< 0.1%
20 2
0.1%
16 2
0.1%
13 2
0.1%

bathrooms
Real number (ℝ)

Distinct22
Distinct (%)0.6%
Missing29
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.6248581
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:46.373766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum99
Range98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6964965
Coefficient of variation (CV)1.0272923
Kurtosis581.70698
Mean2.6248581
Median Absolute Deviation (MAD)1
Skewness19.819461
Sum9250
Variance7.2710935
MonotonicityNot monotonic
2023-07-11T06:46:46.581184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 1123
31.6%
3 979
27.6%
1 779
21.9%
4 446
 
12.6%
5 111
 
3.1%
6 33
 
0.9%
7 20
 
0.6%
8 10
 
0.3%
9 3
 
0.1%
12 3
 
0.1%
Other values (12) 17
 
0.5%
(Missing) 29
 
0.8%
ValueCountFrequency (%)
1 779
21.9%
2 1123
31.6%
3 979
27.6%
4 446
 
12.6%
5 111
 
3.1%
6 33
 
0.9%
7 20
 
0.6%
8 10
 
0.3%
9 3
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
99 1
< 0.1%
57 1
< 0.1%
54 1
< 0.1%
50 1
< 0.1%
36 1
< 0.1%
21 1
< 0.1%
20 2
0.1%
16 1
< 0.1%
15 2
0.1%
13 1
< 0.1%

land_size_m2
Real number (ℝ)

Distinct481
Distinct (%)13.5%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean204.80681
Minimum12
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:46.819651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile55
Q175
median108
Q3192
95-th percentile585
Maximum8000
Range7988
Interquartile range (IQR)117

Descriptive statistics

Standard deviation402.12775
Coefficient of variation (CV)1.9634491
Kurtosis128.26689
Mean204.80681
Median Absolute Deviation (MAD)39
Skewness9.6887812
Sum727269
Variance161706.72
MonotonicityNot monotonic
2023-07-11T06:46:47.072351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 222
 
6.2%
60 220
 
6.2%
90 168
 
4.7%
84 124
 
3.5%
120 116
 
3.3%
105 87
 
2.4%
144 87
 
2.4%
70 76
 
2.1%
180 66
 
1.9%
200 66
 
1.9%
Other values (471) 2319
65.3%
ValueCountFrequency (%)
12 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 2
 
0.1%
18 5
0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
21 3
 
0.1%
22 11
0.3%
23 3
 
0.1%
ValueCountFrequency (%)
8000 1
< 0.1%
7400 1
< 0.1%
5930 1
< 0.1%
5360 1
< 0.1%
5250 1
< 0.1%
5025 1
< 0.1%
4704 1
< 0.1%
4640 1
< 0.1%
4600 1
< 0.1%
4445 1
< 0.1%

building_size_m2
Real number (ℝ)

Distinct358
Distinct (%)10.1%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean186.58744
Minimum1
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:47.327511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q165.5
median112
Q3208
95-th percentile550
Maximum6000
Range5999
Interquartile range (IQR)142.5

Descriptive statistics

Standard deviation248.44347
Coefficient of variation (CV)1.3315123
Kurtosis139.16175
Mean186.58744
Median Absolute Deviation (MAD)58
Skewness8.289543
Sum662572
Variance61724.158
MonotonicityNot monotonic
2023-07-11T06:46:47.574130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 144
 
4.1%
200 125
 
3.5%
60 111
 
3.1%
90 111
 
3.1%
150 109
 
3.1%
45 104
 
2.9%
70 92
 
2.6%
120 90
 
2.5%
100 86
 
2.4%
40 75
 
2.1%
Other values (348) 2504
70.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
22 4
0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
27 4
0.1%
28 1
 
< 0.1%
29 5
0.1%
ValueCountFrequency (%)
6000 1
 
< 0.1%
5000 1
 
< 0.1%
3000 1
 
< 0.1%
2800 1
 
< 0.1%
2417 1
 
< 0.1%
2191 1
 
< 0.1%
2000 1
 
< 0.1%
1800 1
 
< 0.1%
1797 1
 
< 0.1%
1500 7
0.2%

carports
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.197861
Minimum0
Maximum15
Zeros757
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:47.779433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.114996
Coefficient of variation (CV)0.93082258
Kurtosis29.201621
Mean1.197861
Median Absolute Deviation (MAD)1
Skewness3.6830891
Sum4256
Variance1.2432161
MonotonicityNot monotonic
2023-07-11T06:46:47.961718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1770
49.8%
2 845
23.8%
0 757
21.3%
3 77
 
2.2%
4 61
 
1.7%
6 16
 
0.5%
5 9
 
0.3%
10 7
 
0.2%
8 6
 
0.2%
14 2
 
0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0 757
21.3%
1 1770
49.8%
2 845
23.8%
3 77
 
2.2%
4 61
 
1.7%
5 9
 
0.3%
6 16
 
0.5%
7 1
 
< 0.1%
8 6
 
0.2%
10 7
 
0.2%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 2
 
0.1%
12 1
 
< 0.1%
10 7
 
0.2%
8 6
 
0.2%
7 1
 
< 0.1%
6 16
 
0.5%
5 9
 
0.3%
4 61
1.7%
3 77
2.2%

certificate
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)0.1%
Missing141
Missing (%)4.0%
Memory size27.9 KiB
shm - sertifikat hak milik
3001 
hgb - hak guna bangunan
 
209
lainnya (ppjb,girik,adat,dll)
 
201
hp - hak pakai
 
1

Length

Max length29
Median length26
Mean length25.989449
Min length14

Characters and Unicode

Total characters88676
Distinct characters24
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowshm - sertifikat hak milik
2nd rowhgb - hak guna bangunan
3rd rowhgb - hak guna bangunan
4th rowshm - sertifikat hak milik
5th rowshm - sertifikat hak milik

Common Values

ValueCountFrequency (%)
shm - sertifikat hak milik 3001
84.5%
hgb - hak guna bangunan 209
 
5.9%
lainnya (ppjb,girik,adat,dll) 201
 
5.7%
hp - hak pakai 1
 
< 0.1%
(Missing) 141
 
4.0%

Length

2023-07-11T06:46:48.178468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T06:46:48.403335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3211
19.5%
hak 3211
19.5%
shm 3001
18.2%
sertifikat 3001
18.2%
milik 3001
18.2%
hgb 209
 
1.3%
guna 209
 
1.3%
bangunan 209
 
1.3%
lainnya 201
 
1.2%
ppjb,girik,adat,dll 201
 
1.2%
Other values (2) 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
13044
14.7%
i 12608
14.2%
k 9415
10.6%
a 7645
8.6%
h 6422
7.2%
t 6203
7.0%
s 6002
6.8%
m 6002
6.8%
l 3604
 
4.1%
- 3211
 
3.6%
Other values (14) 14520
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71416
80.5%
Space Separator 13044
 
14.7%
Dash Punctuation 3211
 
3.6%
Other Punctuation 603
 
0.7%
Open Punctuation 201
 
0.2%
Close Punctuation 201
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 12608
17.7%
k 9415
13.2%
a 7645
10.7%
h 6422
9.0%
t 6203
8.7%
s 6002
8.4%
m 6002
8.4%
l 3604
 
5.0%
r 3202
 
4.5%
f 3001
 
4.2%
Other values (9) 7312
10.2%
Space Separator
ValueCountFrequency (%)
13044
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3211
100.0%
Other Punctuation
ValueCountFrequency (%)
, 603
100.0%
Open Punctuation
ValueCountFrequency (%)
( 201
100.0%
Close Punctuation
ValueCountFrequency (%)
) 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71416
80.5%
Common 17260
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 12608
17.7%
k 9415
13.2%
a 7645
10.7%
h 6422
9.0%
t 6203
8.7%
s 6002
8.4%
m 6002
8.4%
l 3604
 
5.0%
r 3202
 
4.5%
f 3001
 
4.2%
Other values (9) 7312
10.2%
Common
ValueCountFrequency (%)
13044
75.6%
- 3211
 
18.6%
, 603
 
3.5%
( 201
 
1.2%
) 201
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13044
14.7%
i 12608
14.2%
k 9415
10.6%
a 7645
8.6%
h 6422
7.2%
t 6203
7.0%
s 6002
6.8%
m 6002
6.8%
l 3604
 
4.1%
- 3211
 
3.6%
Other values (14) 14520
16.4%

electricity
Categorical

Distinct30
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
2200 mah
1390 
1300 mah
807 
lainnya mah
294 
4400 mah
269 
3500 mah
261 
Other values (25)
532 

Length

Max length11
Median length8
Mean length8.2780749
Min length7

Characters and Unicode

Total characters29412
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row4400 mah
2nd row2200 mah
3rd row2200 mah
4th row3500 mah
5th row3500 mah

Common Values

ValueCountFrequency (%)
2200 mah 1390
39.1%
1300 mah 807
22.7%
lainnya mah 294
 
8.3%
4400 mah 269
 
7.6%
3500 mah 261
 
7.3%
5500 mah 184
 
5.2%
3300 mah 94
 
2.6%
7700 mah 63
 
1.8%
6600 mah 38
 
1.1%
11000 mah 26
 
0.7%
Other values (20) 127
 
3.6%

Length

2023-07-11T06:46:48.607498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mah 3553
50.0%
2200 1390
 
19.6%
1300 807
 
11.4%
lainnya 294
 
4.1%
4400 269
 
3.8%
3500 261
 
3.7%
5500 184
 
2.6%
3300 94
 
1.3%
7700 63
 
0.9%
6600 38
 
0.5%
Other values (21) 153
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 6609
22.5%
a 4141
14.1%
3553
12.1%
m 3553
12.1%
h 3553
12.1%
2 2804
9.5%
3 1299
 
4.4%
1 937
 
3.2%
5 661
 
2.2%
n 588
 
2.0%
Other values (8) 1714
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13142
44.7%
Lowercase Letter 12717
43.2%
Space Separator 3553
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6609
50.3%
2 2804
21.3%
3 1299
 
9.9%
1 937
 
7.1%
5 661
 
5.0%
4 544
 
4.1%
7 138
 
1.1%
6 124
 
0.9%
9 20
 
0.2%
8 6
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 4141
32.6%
m 3553
27.9%
h 3553
27.9%
n 588
 
4.6%
i 294
 
2.3%
y 294
 
2.3%
l 294
 
2.3%
Space Separator
ValueCountFrequency (%)
3553
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16695
56.8%
Latin 12717
43.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6609
39.6%
3553
21.3%
2 2804
16.8%
3 1299
 
7.8%
1 937
 
5.6%
5 661
 
4.0%
4 544
 
3.3%
7 138
 
0.8%
6 124
 
0.7%
9 20
 
0.1%
Latin
ValueCountFrequency (%)
a 4141
32.6%
m 3553
27.9%
h 3553
27.9%
n 588
 
4.6%
i 294
 
2.3%
y 294
 
2.3%
l 294
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6609
22.5%
a 4141
14.1%
3553
12.1%
m 3553
12.1%
h 3553
12.1%
2 2804
9.5%
3 1299
 
4.4%
1 937
 
3.2%
5 661
 
2.2%
n 588
 
2.0%
Other values (8) 1714
 
5.8%

maid_bedrooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49648185
Minimum0
Maximum7
Zeros2078
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:48.811416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.68572273
Coefficient of variation (CV)1.3811637
Kurtosis7.1896447
Mean0.49648185
Median Absolute Deviation (MAD)0
Skewness1.8656329
Sum1764
Variance0.47021566
MonotonicityNot monotonic
2023-07-11T06:46:48.979223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 2078
58.5%
1 1252
35.2%
2 179
 
5.0%
3 31
 
0.9%
4 8
 
0.2%
5 2
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 2078
58.5%
1 1252
35.2%
2 179
 
5.0%
3 31
 
0.9%
4 8
 
0.2%
5 2
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
0.1%
5 2
 
0.1%
4 8
 
0.2%
3 31
 
0.9%
2 179
 
5.0%
1 1252
35.2%
0 2078
58.5%

maid_bathrooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37039122
Minimum0
Maximum5
Zeros2313
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:49.152500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53602357
Coefficient of variation (CV)1.4471822
Kurtosis3.045368
Mean0.37039122
Median Absolute Deviation (MAD)0
Skewness1.3414687
Sum1316
Variance0.28732127
MonotonicityNot monotonic
2023-07-11T06:46:49.318558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2313
65.1%
1 1179
33.2%
2 50
 
1.4%
3 8
 
0.2%
4 2
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 2313
65.1%
1 1179
33.2%
2 50
 
1.4%
3 8
 
0.2%
4 2
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 2
 
0.1%
3 8
 
0.2%
2 50
 
1.4%
1 1179
33.2%
0 2313
65.1%

floors
Categorical

Distinct5
Distinct (%)0.1%
Missing6
Missing (%)0.2%
Memory size27.9 KiB
2.0
1986 
1.0
1210 
3.0
329 
4.0
 
20
5.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10641
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 1986
55.9%
1.0 1210
34.1%
3.0 329
 
9.3%
4.0 20
 
0.6%
5.0 2
 
0.1%
(Missing) 6
 
0.2%

Length

2023-07-11T06:46:49.503660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T06:46:49.729108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1986
56.0%
1.0 1210
34.1%
3.0 329
 
9.3%
4.0 20
 
0.6%
5.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 3547
33.3%
0 3547
33.3%
2 1986
18.7%
1 1210
 
11.4%
3 329
 
3.1%
4 20
 
0.2%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7094
66.7%
Other Punctuation 3547
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3547
50.0%
2 1986
28.0%
1 1210
 
17.1%
3 329
 
4.6%
4 20
 
0.3%
5 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10641
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3547
33.3%
0 3547
33.3%
2 1986
18.7%
1 1210
 
11.4%
3 329
 
3.1%
4 20
 
0.2%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3547
33.3%
0 3547
33.3%
2 1986
18.7%
1 1210
 
11.4%
3 329
 
3.1%
4 20
 
0.2%
5 2
 
< 0.1%

building_age
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct42
Distinct (%)2.0%
Missing1445
Missing (%)40.7%
Infinite0
Infinite (%)0.0%
Mean3.8809298
Minimum0
Maximum152
Zeros1051
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:49.946816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile17
Maximum152
Range152
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.6037083
Coefficient of variation (CV)1.9592491
Kurtosis98.466882
Mean3.8809298
Median Absolute Deviation (MAD)1
Skewness6.8072336
Sum8181
Variance57.81638
MonotonicityNot monotonic
2023-07-11T06:46:50.185969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 1051
29.6%
1 199
 
5.6%
7 125
 
3.5%
2 106
 
3.0%
4 81
 
2.3%
3 68
 
1.9%
5 63
 
1.8%
12 62
 
1.7%
6 58
 
1.6%
8 43
 
1.2%
Other values (32) 252
 
7.1%
(Missing) 1445
40.7%
ValueCountFrequency (%)
0 1051
29.6%
1 199
 
5.6%
2 106
 
3.0%
3 68
 
1.9%
4 81
 
2.3%
5 63
 
1.8%
6 58
 
1.6%
7 125
 
3.5%
8 43
 
1.2%
9 28
 
0.8%
ValueCountFrequency (%)
152 1
< 0.1%
121 1
< 0.1%
52 1
< 0.1%
49 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
42 2
0.1%
40 1
< 0.1%
37 1
< 0.1%
36 2
0.1%

year_built
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)2.2%
Missing1445
Missing (%)40.7%
Infinite0
Infinite (%)0.0%
Mean2018.1371
Minimum1870
Maximum2052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:50.425581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1870
5-th percentile2005
Q12016
median2021
Q32022
95-th percentile2022
Maximum2052
Range182
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.6414483
Coefficient of variation (CV)0.0037863871
Kurtosis96.724639
Mean2018.1371
Median Absolute Deviation (MAD)1
Skewness-6.6719543
Sum4254233
Variance58.391732
MonotonicityNot monotonic
2023-07-11T06:46:50.660683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2022 1046
29.4%
2021 199
 
5.6%
2015 125
 
3.5%
2020 106
 
3.0%
2018 81
 
2.3%
2019 68
 
1.9%
2017 63
 
1.8%
2010 62
 
1.7%
2016 58
 
1.6%
2014 43
 
1.2%
Other values (36) 257
 
7.2%
(Missing) 1445
40.7%
ValueCountFrequency (%)
1870 1
< 0.1%
1901 1
< 0.1%
1970 1
< 0.1%
1973 1
< 0.1%
1976 1
< 0.1%
1977 1
< 0.1%
1980 2
0.1%
1982 1
< 0.1%
1985 1
< 0.1%
1986 2
0.1%
ValueCountFrequency (%)
2052 1
 
< 0.1%
2025 1
 
< 0.1%
2024 2
 
0.1%
2023 1
 
< 0.1%
2022 1046
29.4%
2021 199
 
5.6%
2020 106
 
3.0%
2019 68
 
1.9%
2018 81
 
2.3%
2017 63
 
1.8%

property_condition
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.2%
Missing246
Missing (%)6.9%
Memory size27.9 KiB
bagus
1437 
baru
1328 
bagus sekali
261 
sudah renovasi
158 
butuh renovasi
 
94
Other values (2)
 
29

Length

Max length14
Median length12
Mean length5.8929543
Min length4

Characters and Unicode

Total characters19488
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbagus
2nd rowbagus
3rd rowbagus
4th rowbagus sekali
5th rowbagus

Common Values

ValueCountFrequency (%)
bagus 1437
40.4%
baru 1328
37.4%
bagus sekali 261
 
7.3%
sudah renovasi 158
 
4.4%
butuh renovasi 94
 
2.6%
unfurnished 25
 
0.7%
semi furnished 4
 
0.1%
(Missing) 246
 
6.9%

Length

2023-07-11T06:46:50.986855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T06:46:51.340219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bagus 1698
44.4%
baru 1328
34.7%
sekali 261
 
6.8%
renovasi 252
 
6.6%
sudah 158
 
4.1%
butuh 94
 
2.5%
unfurnished 25
 
0.7%
semi 4
 
0.1%
furnished 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 3697
19.0%
u 3426
17.6%
b 3120
16.0%
s 2402
12.3%
g 1698
8.7%
r 1609
8.3%
e 546
 
2.8%
i 546
 
2.8%
517
 
2.7%
n 306
 
1.6%
Other values (9) 1621
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18971
97.3%
Space Separator 517
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3697
19.5%
u 3426
18.1%
b 3120
16.4%
s 2402
12.7%
g 1698
9.0%
r 1609
8.5%
e 546
 
2.9%
i 546
 
2.9%
n 306
 
1.6%
h 281
 
1.5%
Other values (8) 1340
 
7.1%
Space Separator
ValueCountFrequency (%)
517
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18971
97.3%
Common 517
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3697
19.5%
u 3426
18.1%
b 3120
16.4%
s 2402
12.7%
g 1698
9.0%
r 1609
8.5%
e 546
 
2.9%
i 546
 
2.9%
n 306
 
1.6%
h 281
 
1.5%
Other values (8) 1340
 
7.1%
Common
ValueCountFrequency (%)
517
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3697
19.0%
u 3426
17.6%
b 3120
16.0%
s 2402
12.3%
g 1698
8.7%
r 1609
8.3%
e 546
 
2.8%
i 546
 
2.8%
517
 
2.7%
n 306
 
1.6%
Other values (9) 1621
8.3%
Distinct8
Distinct (%)0.4%
Missing1647
Missing (%)46.4%
Memory size27.9 KiB
selatan
562 
timur
530 
utara
405 
barat
204 
barat daya
67 
Other values (3)
138 

Length

Max length10
Median length5
Mean length6.0823715
Min length5

Characters and Unicode

Total characters11593
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowutara
2nd rowutara
3rd rowselatan
4th rowutara
5th rowselatan

Common Values

ValueCountFrequency (%)
selatan 562
 
15.8%
timur 530
 
14.9%
utara 405
 
11.4%
barat 204
 
5.7%
barat daya 67
 
1.9%
timur laut 67
 
1.9%
tenggara 43
 
1.2%
barat laut 28
 
0.8%
(Missing) 1647
46.4%

Length

2023-07-11T06:46:51.684196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T06:46:52.126677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
timur 597
28.9%
selatan 562
27.2%
utara 405
19.6%
barat 299
14.5%
laut 95
 
4.6%
daya 67
 
3.2%
tenggara 43
 
2.1%

Most occurring characters

ValueCountFrequency (%)
a 2847
24.6%
t 2001
17.3%
r 1344
11.6%
u 1097
 
9.5%
l 657
 
5.7%
e 605
 
5.2%
n 605
 
5.2%
i 597
 
5.1%
m 597
 
5.1%
s 562
 
4.8%
Other values (5) 681
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11431
98.6%
Space Separator 162
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2847
24.9%
t 2001
17.5%
r 1344
11.8%
u 1097
 
9.6%
l 657
 
5.7%
e 605
 
5.3%
n 605
 
5.3%
i 597
 
5.2%
m 597
 
5.2%
s 562
 
4.9%
Other values (4) 519
 
4.5%
Space Separator
ValueCountFrequency (%)
162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11431
98.6%
Common 162
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2847
24.9%
t 2001
17.5%
r 1344
11.8%
u 1097
 
9.6%
l 657
 
5.7%
e 605
 
5.3%
n 605
 
5.3%
i 597
 
5.2%
m 597
 
5.2%
s 562
 
4.9%
Other values (4) 519
 
4.5%
Common
ValueCountFrequency (%)
162
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2847
24.6%
t 2001
17.3%
r 1344
11.6%
u 1097
 
9.5%
l 657
 
5.7%
e 605
 
5.2%
n 605
 
5.2%
i 597
 
5.1%
m 597
 
5.1%
s 562
 
4.8%
Other values (5) 681
 
5.9%

garages
Real number (ℝ)

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70897833
Minimum0
Maximum50
Zeros1921
Zeros (%)54.1%
Negative0
Negative (%)0.0%
Memory size27.9 KiB
2023-07-11T06:46:52.494480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum50
Range50
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3118787
Coefficient of variation (CV)1.8503792
Kurtosis577.56061
Mean0.70897833
Median Absolute Deviation (MAD)0
Skewness16.851259
Sum2519
Variance1.7210258
MonotonicityNot monotonic
2023-07-11T06:46:52.861768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1921
54.1%
1 1018
28.7%
2 519
 
14.6%
4 42
 
1.2%
3 31
 
0.9%
6 10
 
0.3%
5 6
 
0.2%
10 3
 
0.1%
12 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
0 1921
54.1%
1 1018
28.7%
2 519
 
14.6%
3 31
 
0.9%
4 42
 
1.2%
5 6
 
0.2%
6 10
 
0.3%
10 3
 
0.1%
12 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
20 1
 
< 0.1%
12 1
 
< 0.1%
10 3
 
0.1%
6 10
 
0.3%
5 6
 
0.2%
4 42
 
1.2%
3 31
 
0.9%
2 519
14.6%
1 1018
28.7%

furnishing
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.1%
Missing387
Missing (%)10.9%
Memory size27.9 KiB
unfurnished
2066 
semi furnished
833 
furnished
238 
baru
 
29

Length

Max length14
Median length11
Mean length11.574858
Min length4

Characters and Unicode

Total characters36646
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunfurnished
2nd rowunfurnished
3rd rowunfurnished
4th rowunfurnished
5th rowsemi furnished

Common Values

ValueCountFrequency (%)
unfurnished 2066
58.1%
semi furnished 833
23.4%
furnished 238
 
6.7%
baru 29
 
0.8%
(Missing) 387
 
10.9%

Length

2023-07-11T06:46:53.264517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T06:46:53.674777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unfurnished 2066
51.7%
furnished 1071
26.8%
semi 833
20.8%
baru 29
 
0.7%

Most occurring characters

ValueCountFrequency (%)
u 5232
14.3%
n 5203
14.2%
i 3970
10.8%
s 3970
10.8%
e 3970
10.8%
r 3166
8.6%
f 3137
8.6%
h 3137
8.6%
d 3137
8.6%
m 833
 
2.3%
Other values (3) 891
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35813
97.7%
Space Separator 833
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 5232
14.6%
n 5203
14.5%
i 3970
11.1%
s 3970
11.1%
e 3970
11.1%
r 3166
8.8%
f 3137
8.8%
h 3137
8.8%
d 3137
8.8%
m 833
 
2.3%
Other values (2) 58
 
0.2%
Space Separator
ValueCountFrequency (%)
833
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35813
97.7%
Common 833
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 5232
14.6%
n 5203
14.5%
i 3970
11.1%
s 3970
11.1%
e 3970
11.1%
r 3166
8.8%
f 3137
8.8%
h 3137
8.8%
d 3137
8.8%
m 833
 
2.3%
Other values (2) 58
 
0.2%
Common
ValueCountFrequency (%)
833
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 5232
14.3%
n 5203
14.2%
i 3970
10.8%
s 3970
10.8%
e 3970
10.8%
r 3166
8.6%
f 3137
8.6%
h 3137
8.6%
d 3137
8.6%
m 833
 
2.3%
Other values (3) 891
 
2.4%

Interactions

2023-07-11T06:46:36.173419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:45:59.220883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:02.625401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:06.027003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:08.775654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:12.164082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:15.434034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:18.410656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:21.097481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:23.731786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:27.557256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:30.505210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:33.301841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:36.390425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:45:59.581672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:02.934599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:06.235118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:08.979736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:12.494149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:15.644548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:18.615120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:21.291023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:23.977833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:27.772037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:30.719325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:33.531738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:36.600130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:45:59.911717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:03.334139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:06.474290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:09.360374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:12.858111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:15.887936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:18.844726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:21.505168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:24.274053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:27.984711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:30.949767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:33.767748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:36.796844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:00.278142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:03.657422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:06.685046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:09.582252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:13.186119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:16.103573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:19.055001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:21.708309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:24.593781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:28.198848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:31.171782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:33.986146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:36.987494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:00.573069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:03.995538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:06.881665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:09.761340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:13.518248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:16.303232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:19.243790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:21.899523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:24.920076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:28.391370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:31.372263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:34.197926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:37.179476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:00.767265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:04.268123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:07.078591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:09.962089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:13.822144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:16.506491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:19.435968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:22.098532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:25.198567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:28.854881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:31.580042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:34.401900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:37.489462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:00.988954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:04.499448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:07.300367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:10.191960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:14.044591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:16.724704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:19.651143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:22.305216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:25.557549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:29.073782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:31.774005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:34.616615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:37.741059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:01.193499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:04.721233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:07.514271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:10.406460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:14.236885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:16.947733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:19.870563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:22.507180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:25.889572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:29.282023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:31.992002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:34.839972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:38.014352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:01.404145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:04.945214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:07.717701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:10.652805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:14.438489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:17.162890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:20.081244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:22.704384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:26.217605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:29.487770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:32.197895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:35.065386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:38.357687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:01.640264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:05.161167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:07.937581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:10.970541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:14.637768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:17.370625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:20.282388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:22.911341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:26.529044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:29.689347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:32.433869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:35.283572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:38.670015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:01.832613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:05.362203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:08.133900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:11.235979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:14.845887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:17.574274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:20.469631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:23.114922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:26.809991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:29.874502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:32.647197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:35.527357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:38.975049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:02.050366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:05.588875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:08.361996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:11.540515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:15.046095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:17.788885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:20.688034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:23.330977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:27.132233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:30.090773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:32.861125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:35.745516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:39.288254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:02.273793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:05.823494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:08.585318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:11.886437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:15.243940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:18.206068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:20.904753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:23.536431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:27.360625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:30.315071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:33.076477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T06:46:35.976695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-11T06:46:53.976743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
price_in_rplatlongbedroomsbathroomsland_size_m2building_size_m2carportsmaid_bedroomsmaid_bathroomsbuilding_ageyear_builtgaragescitycertificateelectricityfloorsproperty_conditionbuilding_orientationfurnishing
price_in_rp1.0000.240-0.2640.7400.7990.8230.9040.3190.6420.6610.451-0.4510.2320.1000.0000.2490.0300.0380.0000.008
lat0.2401.000-0.1720.1420.1730.0320.185-0.0130.0890.1460.105-0.106-0.0450.4680.1420.1610.1970.0910.0830.164
long-0.264-0.1721.000-0.189-0.187-0.100-0.160-0.033-0.115-0.148-0.0100.009-0.0450.4350.0670.0000.0580.0350.0890.078
bedrooms0.7400.142-0.1891.0000.7890.6500.8000.2820.4640.4800.322-0.3220.2320.0610.0000.3360.4540.0180.0000.083
bathrooms0.7990.173-0.1870.7891.0000.6410.8140.3220.5350.5320.255-0.2560.2350.0570.0000.4390.4600.0190.0000.082
land_size_m20.8230.032-0.1000.6500.6411.0000.8420.2860.5930.5900.589-0.5890.2580.0980.0300.3490.0410.0650.0330.052
building_size_m20.9040.185-0.1600.8000.8140.8421.0000.3010.6240.6270.501-0.5010.2470.1450.0000.2550.2940.0710.0330.096
carports0.319-0.013-0.0330.2820.3220.2860.3011.0000.2920.3410.067-0.0670.1540.1190.0000.3590.1640.0830.0000.111
maid_bedrooms0.6420.089-0.1150.4640.5350.5930.6240.2921.0000.7920.412-0.4110.2060.1690.0750.3840.1850.0980.0870.131
maid_bathrooms0.6610.146-0.1480.4800.5320.5900.6270.3410.7921.0000.388-0.3880.2060.1920.0760.4040.2000.0840.0990.176
building_age0.4510.105-0.0100.3220.2550.5890.5010.0670.4120.3881.000-0.9990.1060.0840.0600.1960.0000.2170.0000.036
year_built-0.451-0.1060.009-0.322-0.256-0.589-0.501-0.067-0.411-0.388-0.9991.000-0.1060.1140.0430.2020.0000.2920.0870.084
garages0.232-0.045-0.0450.2320.2350.2580.2470.1540.2060.2060.106-0.1061.0000.0780.0000.2200.3840.0440.0000.070
city0.1000.4680.4350.0610.0570.0980.1450.1190.1690.1920.0840.1140.0781.0000.1580.2340.2330.1160.1080.192
certificate0.0000.1420.0670.0000.0000.0300.0000.0000.0750.0760.0600.0430.0000.1581.0000.0760.0640.0430.0880.041
electricity0.2490.1610.0000.3360.4390.3490.2550.3590.3840.4040.1960.2020.2200.2340.0761.0000.3270.1820.0760.315
floors0.0300.1970.0580.4540.4600.0410.2940.1640.1850.2000.0000.0000.3840.2330.0640.3271.0000.0760.0890.164
property_condition0.0380.0910.0350.0180.0190.0650.0710.0830.0980.0840.2170.2920.0440.1160.0430.1820.0761.0000.1120.594
building_orientation0.0000.0830.0890.0000.0000.0330.0330.0000.0870.0990.0000.0870.0000.1080.0880.0760.0890.1121.0000.069
furnishing0.0080.1640.0780.0830.0820.0520.0960.1110.1310.1760.0360.0840.0700.1920.0410.3150.1640.5940.0691.000

Missing values

2023-07-11T06:46:39.801249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T06:46:40.850327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-11T06:46:41.672424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

urlprice_in_rptitleaddressdistrictcitylatlongfacilitiesproperty_typeads_idbedroomsbathroomsland_size_m2building_size_m2carportscertificateelectricitymaid_bedroomsmaid_bathroomsfloorsbuilding_ageyear_builtproperty_conditionbuilding_orientationgaragesfurnishing
0https://www.rumah123.com/properti/bekasi/hos11360272/#qid~213b5619-a399-47b3-bfcf-faaef6b542d52.990000e+09Rumah cantik Sumarecon Bekasi\nLingkungan asri, tenang & nyamanSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Tempat Jemuran, Jalur Telepon, Taman, Tamanrumahhos113602724.04.0239.0272.00shm - sertifikat hak milik4400 mah012.05.02017.0bagusNaN0unfurnished
1https://www.rumah123.com/properti/bekasi/hos10680347/#qid~748f5d2d-8d3a-4a4e-a1b4-37c7be7ffc251.270000e+09Rumah Kekinian, Magenta Summarecon BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Tamanrumahhos106803473.02.055.069.01hgb - hak guna bangunan2200 mah002.0NaNNaNbagusNaN0NaN
2https://www.rumah123.com/properti/bekasi/hos10685867/#qid~f6c2cf9d-44fa-4c1a-8749-8ee96b116e931.950000e+09Rumah Cantik 2 Lantai Cluster Bluebell Summarecon BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Jogging Track, Kolam Renang, Masjid, Taman, Jalur Telepon, Keamananrumahhos106858673.03.0119.0131.01hgb - hak guna bangunan2200 mah112.0NaNNaNbagusNaN1unfurnished
3https://www.rumah123.com/properti/bekasi/hos10927790/#qid~f6c2cf9d-44fa-4c1a-8749-8ee96b116e933.300000e+09Rumah Mewah 2Lantai L10x18 C di Cluster VERNONIA Summarecon Bekasi..Summarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Jalur Telepon, Jogging Track, Track Lari, Kolam Renang, Kolam Renang, Taman, Taman, CCTV, Jalur Telepon, Keamananrumahhos109277903.03.0180.0174.00shm - sertifikat hak milik3500 mah112.06.02016.0bagus sekaliutara2unfurnished
4https://www.rumah123.com/properti/bekasi/hos10785530/#qid~1807f915-9393-4e9c-a6d6-eb3d73117a154.500000e+09Rumah Hoek di Cluster Maple Summarecon Bekasi, BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Jogging Track, Kolam Renang, Taman, Jalur Telepon, Keamananrumahhos107855304.03.0328.0196.02shm - sertifikat hak milik3500 mah112.09.02013.0bagusutara1unfurnished
5https://www.rumah123.com/properti/bekasi/hos11177142/#qid~1807f915-9393-4e9c-a6d6-eb3d73117a152.700000e+09Rumah Siap Huni di Cluster Acasia, Summarecon BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Jalur Telepon, Taman, Keamanan, Kolam Renangrumahhos111771423.03.0136.0200.02shm - sertifikat hak milik3500 mah112.09.02013.0bagusselatan1semi furnished
6https://www.rumah123.com/properti/bekasi/hos10720117/#qid~35c27ac2-b166-4762-83af-d2178576ef1b2.350000e+09Rumah di Cluster Maple Summarecon BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Keamanan, Kolam Renangrumahhos107201172.02.0144.0144.01hgb - hak guna bangunan4400 mah002.0NaNNaNNaNutara1NaN
7https://www.rumah123.com/properti/bekasi/hos10753439/#qid~35c27ac2-b166-4762-83af-d2178576ef1b4.500000e+09Rumah Cluster Lotus Summarecon BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Keamanan, Kolam Renangrumahhos107534394.04.0216.0250.02hgb - hak guna bangunan3500 mah112.0NaNNaNNaNselatan1NaN
8https://www.rumah123.com/properti/bekasi/hos11127849/#qid~35c27ac2-b166-4762-83af-d2178576ef1b2.900000e+09Rumah Cluster Palm Summarecon BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Kitchen Setrumahhos11127849NaN3.0200.0152.02shm - sertifikat hak milik4400 mah312.0NaNNaNNaNselatan0semi furnished
9https://www.rumah123.com/properti/bekasi/hos11169103/#qid~35c27ac2-b166-4762-83af-d2178576ef1b2.700000e+09Rumah Cluster Acacia Summarecon BekasiSummarecon Bekasi, BekasiSummarecon BekasiBekasi-6.223945106.986275Keamanan 24 jam, Kolam Renangrumahhos111691033.03.0136.0200.01shm - sertifikat hak milik3500 mah112.0NaNNaNNaNselatan1semi furnished
urlprice_in_rptitleaddressdistrictcitylatlongfacilitiesproperty_typeads_idbedroomsbathroomsland_size_m2building_size_m2carportscertificateelectricitymaid_bedroomsmaid_bathroomsfloorsbuilding_ageyear_builtproperty_conditionbuilding_orientationgaragesfurnishing
3543https://www.rumah123.com/properti/tangerang/hos11364171/#qid~de3482b1-2665-4625-8388-e73221c6121f7.850000e+08Rumah Elegan 2 Lantai SHM Lokasi StrategisCimone, TangerangCimoneTangerang-6.190701106.610469Masjid, Keamanan, Taman, Jogging Track, Tempat Jemuran, Jalur Telepon, Keamanan 24 jam, Track Lari, Akses Parkir, One Gate System, Jalur Teleponrumahhos113641713.02.085.060.01shm - sertifikat hak milik1300 mah002.00.02022.0baruutara0unfurnished
3544https://www.rumah123.com/properti/tangerang/hos11364171/#qid~7724b384-c2e7-421e-85bb-1b5687e654d17.850000e+08Rumah Elegan 2 Lantai SHM Lokasi StrategisCimone, TangerangCimoneTangerang-6.190701106.610469Masjid, Keamanan, Taman, Jogging Track, Tempat Jemuran, Jalur Telepon, Keamanan 24 jam, Track Lari, Akses Parkir, One Gate System, Jalur Teleponrumahhos113641713.02.085.060.01shm - sertifikat hak milik1300 mah002.00.02022.0baruutara0unfurnished
3545https://www.rumah123.com/properti/tangerang/hos11363405/#qid~7abe792b-a7f3-4c78-9517-85af54405a536.850000e+08Rumah Mewah Murah 2 Lantai SHM Free Biaya Biaya Surat Bebas BanjirBabakan, TangerangBabakanTangerang-6.190893106.635970Akses Parkir, Masjid, Jogging Track, Taman, Tempat Jemuran, Jalur Telepon, Keamanan, Keamanan 24 jam, Masjid, Track Lari, Taman, One Gate System, Jalur Teleponrumahhos113634053.02.064.060.01shm - sertifikat hak milik1300 mah002.00.02022.0barutimur0unfurnished
3546https://www.rumah123.com/properti/tangerang/hos11361469/#qid~9b7c3e11-2c18-4559-a637-16b29b361aad1.200000e+09Dijual Rumah 2 Lantai Bagus Cluster di Tangerang Selatan SHMBabakan, TangerangBabakanTangerang-6.190893106.635970Taman, Tempat Jemuran, Jalur Telepon, One Gate System, Keamanan 24 jam, AC, Wastafel, Playgroundrumahhos113614693.02.060.078.01shm - sertifikat hak milik5500 mah002.01.02021.0bagusbarat0unfurnished
3547https://www.rumah123.com/properti/tangerang/hos11361447/#qid~c984c56a-bc1e-499a-afc7-2d459a55dd656.850000e+08Rumah Mewah Murah 2 Lantai Include Biaya Biaya SuratBabakan, TangerangBabakanTangerang-6.190893106.635970Akses Parkir, Masjid, Keamanan, Jogging Track, Taman, Tempat Jemuran, Jalur Telepon, Keamanan 24 jam, Masjid, Track Lari, Taman, One Gate Systemrumahhos113614473.02.064.060.01shm - sertifikat hak milik1300 mah002.00.02022.0barutimur0unfurnished
3548https://www.rumah123.com/properti/tangerang/hos11361759/#qid~9b7c3e11-2c18-4559-a637-16b29b361aad5.880000e+08Terbaru Cluster Minimalis Sudimara Dekat StasiunJombang, TangerangJombangTangerang-6.296615106.704601Masjid, Taman, Tempat Jemuran, Keamanan 24 jam, AC, Wastafel, CCTV, Track Lari, Taman, One Gate Systemrumahhos113617592.01.072.036.01shm - sertifikat hak milik1300 mah001.00.02022.0baruNaN1furnished
3549https://www.rumah123.com/properti/tangerang/hos11359615/#qid~0d9492e8-7326-465d-85ee-e54559fedc267.850000e+08Rumah Modern Asri dan Nyaman Bebas Banjir Lokasi StrategisLengkong Kulon, TangerangLengkong KulonTangerang-6.283454106.638775Masjid, Keamanan, Jogging Track, Taman, Tempat Jemuran, Jalur Telepon, Keamanan 24 jam, Masjid, Track Lari, Taman, One Gate Systemrumahhos113596153.02.085.060.01shm - sertifikat hak milik1300 mah002.00.02022.0baruutara0unfurnished
3550https://www.rumah123.com/properti/tangerang/hos11359594/#qid~0d9492e8-7326-465d-85ee-e54559fedc267.550000e+08Rumah Mewah Murah 2 Lantai Gratis Biaya Biaya Surat Lokasi StrategisLengkong Kulon, TangerangLengkong KulonTangerang-6.283454106.638775Keamanan, Taman, Jogging Track, Tempat Jemuran, Jalur Telepon, Masjid, Track Larirumahhos113595943.02.078.060.01shm - sertifikat hak milik1300 mah002.00.02022.0baruutara0unfurnished
3551https://www.rumah123.com/properti/tangerang/hos11359313/#qid~5d5b05f9-ab0d-4774-ae61-bddd6e38b7f08.000000e+08Rumah 2 Lantai Dekat Perkantoran BSD City AEON TOL Dan StasiunBSD Provance Parkland, TangerangBSD Provance ParklandTangerang-6.288237106.665859Tempat Jemuran, Keamanan, Keamanan 24 jam, Wastafel, Taman, One Gate Systemrumahhos113593133.02.060.065.02shm - sertifikat hak milik2200 mah002.00.02022.0baruselatan0furnished
3552https://www.rumah123.com/properti/tangerang/hos11358964/#qid~5d5b05f9-ab0d-4774-ae61-bddd6e38b7f06.550000e+08Rumah 2 Lantai Harga 1 Lantai Di Pamulang, Free Biaya BiayaSudimara, TangerangSudimaraTangerang-6.296963106.710635Kulkas, Masjid, Taman, Lapangan Bola, Tempat Jemuran, Jalur Telepon, Lapangan Bulu Tangkis, Track Lari, Keamanan 24 jam, Wastafel, Akses Parkir, One Gate System, AC, Tempat Laundry, Playgroundrumahhos113589643.02.064.060.01shm - sertifikat hak milik1300 mah002.00.02022.0barutimur2semi furnished